Analyzing the data sets to summarize their main characteristics of variables, often with visual graphs, without using a statistical model.
Understanding the dimensions of the dataset, variable names, overall missing summary and data types of each variables
# Overview of the data
ExpData(data=data,type=1)
# Structure of the data
ExpData(data=data,type=2)
Overview of the data
Target variable
Summary of categorical dependent variable
Summary of all numerical variables
Summary statistics when dependent variable is categorical SERVICES. Summary statistics will be splitted into category level
ExpNumStat(data,by="GA",gp=Target,Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)
Box plots for all numerical variables vs categorical dependent variable - Bivariate comparision only with categories
Quantile-quantile plot for all Numerical variables
ExpOutQQ(data,nlim=4,fname=NULL,Page=c(2,2),sample=sn)
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Density plot for all Numerical variables
ExpNumViz(data,target=NULL,type=1,Page=c(2,2),theme=theme,sample=sn)
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ExpNumViz(data,target=Target,Page=c(2,1),theme=theme,sample=sn,scatter=TRUE)
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Boxplot for all the numeric attributes by each category of SERVICES
ExpNumViz(data,target=Target,type=2,theme=theme,Page=c(2,2),sample=sn)
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Summary of categorical variable
Cross tabulation with target variable
ExpCTable(data,Target=Target,margin=1,clim=10,nlim=5,round=2,bin=NULL,per=F)
Information Value
ExpCatStat(data,Target=Target,Label=label,result = "IV",clim=10,nlim=5,Pclass=Rc)
Statistical test
ExpCatStat(data,Target=Target,Label=label,result = "Stat",clim=10,nlim=5,Pclass=Rc)
Variable importance based on Information value
varimp <- ExpCatStat(data,Target=Target,result = "Stat",clim=10,nlim=5,Pclass=Rc,bins=10,plot=TRUE,top=30,Round=2)
Graphical representation of all categorical variables
Bar plots for all categorical variables
ExpCatViz(data,target=NULL,fname=NULL,clim=10,margin=2,theme=theme,Page = c(2,1),sample=sc)
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ExpCatViz(data,target=Target,fname=NULL,clim=10,margin=2,theme=theme,Page = c(2,1),sample=sc)
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